The purpose of this study is to eliminate the aliasing artifacts in accerelated radial MRI. We designed a Cross-Domain deep-learning network, called SISI-Net(Sinogram-Image-Sinogram-Image Network). This is an architecture to gradually solves data sparsity problems by iteratively learning the radial sampling data in the sinogram domain and the reconstructed data in the image domain. As a result, proposed network could remove aliasing artifacts effectively while maintaining structural information.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords